class: center, middle, inverse, title-slide .title[ # Ready Made Garments, Reproductive Behavior and Human Capital among Bangladeshi Women ] .subtitle[ ## SM Shihab Siddiqui ] .author[ ### 21 October, 2022 ] --- class: inverse, middle, center # Introduction --- # Against the grain: Bangladeshi FLFP <img src="data:image/png;base64,#jmp_pres_files/figure-html/unnamed-chunk-1-1.svg" style="display: block; margin: auto;" /> - .hi[Female labor force participation (FLFP)] among 20-24 year olds stood at about 49% in 2015 (ADB 2016). --- # Expansion of the garments industry - Bangladeshi .hi[Ready Made Garments (RMG)] grew at about 11% a year since 1991. -- .pull-left[ <img src="data:image/png;base64,#jmp_pres_files/figure-html/unnamed-chunk-2-1.svg" width="100%" style="display: block; margin: auto;" /> ] .pull-right[ - <font size="5"> Accounts for 75-85% of Bangladesh's exports in recent decades. ] -- .pull-right[ - Contributed about 5-6% to GDP in 2019. ] -- .pull-right[ - .hi[About 60% of workers in export oriented RMG industry are women.] </font> ] --- # Could there be a connection? ## FLFP - Textile and affiliated industry always employed relatively more women across different time and place. -- - Mid 1800s England (Burnette, n.d), USA (Field-Hendrey, 1998); developed and developing countries 1981-2008 (Kucera and Tejani, 2014). -- - Women engaged in more spinning and knitting for centuries *(Virginia Postrel, Textiles and the Fabric of Civilization)* -- - Did this "kick" off an overall FLFP transition in lines of Fogli and Veldkamp (2011)? --- # Could there be a connection? ## Reproductive behavior - Labor market opportunitieschanges net benefits of marriage and children *(e.g. Aaronson et al 2014; Greenwood et al 2017).* -- - But fertility transition was already on the way and may have already ushered in a low-fertility enviroment? What about timing? -- ## Human capital accumulation - More schooling if returns to schooling in RMG industry is adequate. Less if potential students are better off working. - Maybe less schooling but more human capital accumulation through increased effort? -- .hi[All these are empirical questions!] --- # Research questions 1. To what extent did the emergence of the RMG industry contribute to the increase in FLFP in Bangladesh? - A question about magnitude. -- 2. What is the effect of the RMG industry on reproductive behavior of women (marriage and fertility)? - A question about sign and magnitude. -- 3. What is the effect of the RMG industry on human capital accumulation of Bangladeshi women? - A question about sign and magnitude. --- # Why do we care? 1. Adds to the literature on manufacturing- and export-led growth. Especially relevant since: -- - General concerns with pre-mature industrialization (Rodrik, 2015) - Reductions in prevalence of women in RMG industry in Bangladesh <footnote>A [Along the lines of what happens as technology improves in a manufacturing sector (Tejani and Kucera, 2021).</footnote> -- 2. Adds to the literature focusing on trade and lives of workers (Autor et al (2013), Li (2018) and Autor et al (2019)). --- # Preview of the paper ## Methods - Estimates the long run impact of female labor demand shock on FLFP, fertility and human capital accumulation by: - Bartik shift-share method to identify labor demand shocks following a methodology similar to .hi[Autor et al (2013).] - Specifically, I exploit product specialization in the RMG industry along the knit versus woven line across sub-districts (Bangladesh administrative level-3) for identification. --- # Preview of the paper ## Results - FLFP, particularly industrial FLFP changes a lot. -- - Not much of an impact on reproductive behavior and fertility overall. -- Results can be rationalized with previous literature under plausible assumptions. --- class: inverse, middle, center # RMG industry in Bangladesh --- # Knit versus Woven prodcuts .pull-left[ .hi[Knit] <img src="data:image/png;base64,#figures//knit1.png" width="70%" style="display: block; margin: auto;" /> - Single yarn looped repeatedly. - HS code 61. - Product examples: Most sweaters, cotton T-shirts. ] -- .pull-right[ .hi[Woven] <img src="data:image/png;base64,#figures//woven1.png" width="68%" style="display: block; margin: auto;" /> - Multiple yarn criss-crossed over and under each other. - HS code 62. - Product examples: Shirts, jackets, pants. ] --- # Knit versus Woven Specialization <img src="data:image/png;base64,#figures//fac_dist.png" width="70%" style="display: block; margin: auto;" /> -- - Producing woven is more energy and capital intensive, and commands about 10% higher per unit price (Sytsma, 2022). -- - Woven factories are larger, and employs more women. --- # Location of RMG factories <img src="data:image/png;base64,#figures//facs_loc.png" width="90%" style="display: block; margin: auto;" /> -- - RMG factory location choice is mostly dependent on infrastructure quality (kagy, 2014). --- count:false class: inverse, middle, center # Identification strategy and data --- # Overview of the identification strategy - Sub-districts with and without factories are likely to have different infrastructure quality, which maybe correlated with outcome variables. So, .hi[I restrict the analysis only to sub-districts that had a factory by 2006.] -- - Different sub-districts have different intensity of knit versus woven specialization **within** the RMG industry. -- - Adopting Goldsmith-Pinkham et al (2020), the key assumption is that these differences in specialization do not change outcomes through confounders. --- # Regression Model `\begin{equation} \Delta Y_{s,t} = \beta \space \Delta \text{Export Exposure}_{s,t} + \delta_{t} + Z_{s,t-1} \beta_z + X_{s,t-1} \beta_x + \epsilon_{s,t} \end{equation}` - `\(\Delta Y_{s,t}\)` is the decadal change in outcome variables in sub-district `\(s\)` over decade ending at year `\(t\)`. -- - `\(\text{Export Exposure}_{s,t}\)` measures export exposure in sub-district over decade ending at year `\(t\)`. -- - `\(\delta_t\)` are period fixed-effects and `\(Z_{s, t-1}\)` is a vector of controls across all outcomes including start of period electrification rate, urbanization, density, share of 15-64 year old in population and years of education for adults (15-64). -- - `\(X_{s,t-1}\)` are start of the period comparable outcomes for males other than in the cases of regressions corresponding to marriage and fertility rates. --- # Export exposure One candidate measure of export exposure per potential worker (Population 15-64) is: `\begin{equation} \sum_{i=0}^{9} \alpha_{s,t-i}^{K} * \frac{\text{Export}_{BD,t-i}^{K}}{L_{s,t-i}} + \sum_{i=1}^{9} \alpha_{s,t-i}^{W} * \frac{\text{Export}_{BD,t-i}^{W}}{{L_{s,t-i}}} \\ \alpha_{s,t-i}^{K} = \frac{Machines_{s,t-i}^{K}}{Machines_{BD,t-i}^{K}}, \alpha_{s,t-i}^{W} = \frac{Machines_{s,t-i}^{W}}{Machines_{BD,t-i}^{W}} \end{equation}` -- - Apportions total knit (woven) exports originating in Bangladesh to a sub-districts based on what share of national knit (woven) production is in the sub-district. -- - Export exposure in sub-district `\(s\)` in at time period `\(t\)` depends on the intensity of knit (woven) specialization and is scaled by total exports and population. --- # Export exposure - But clearly the previous measure can be endogenous since over decades, infrastructure changes and that in itself could change both the shares and change the outcomes differentially. So, I fix share to the values at the start of decade. Thus export exposure per potential worker is as follows: `\begin{align} \Delta \text{ Export Exposure}_{s,t} =& \alpha_{s,t-1}^{K} * \frac{\Delta \space \text{Export}_{BD,t}^{K}}{L_{t-1}} + \alpha_{s,t-1}^{W} * \frac{\Delta \space \text{Export}_{BD,t}^{W}}{L_{t-1}} \end{align}` --- # Identifying Assumptions Adopting from Goldsmith-Pinkham et al (2020), the key assumption is that the differences in knit versus woven specialization do not *change* outcomes through confounders. That is: - .hi[Identifying assumption 1:] Extent of knit versus woven specialization in a sub-district is uncorrelated with the errors conditional on controls in the first difference equations. -- - .hi[Identifying assumption 2:] FLFP responds similarly to woven and knit shocks. -- - .hi[Example of a violation:] Woven employs more women, so areas with more woven factories has increased presence of fertility control programs because there are more women. --- # Data Sources - .hi[Outcome data] is obtained by aggregating individual-level data from the Bangladesh Census 1991 (10% sub-sample), 2001 (10% sub-sample) and 2011 (5% sub-sample). -- - .hi[Factory data] are primarily from multiple BGMEA datasets from members directory (2001, 2010), BGMEA (2015) and scraping of BGMEA websie in 2013. --- class: inverse, middle, center # Results --- # FLFP ## Overall <table class="kable_wrapper"> <caption>Influence on overall (left) and industrial (right) FLFP</caption> <tbody> <tr> <td> <table> <thead> <tr> <th style="text-align:center;"> Age </th> <th style="text-align:center;"> coeff </th> <th style="text-align:center;"> std_error </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 15-64 </td> <td style="text-align:center;"> 0.004 </td> <td style="text-align:center;"> 6e-04 </td> </tr> <tr> <td style="text-align:center;"> 15-29 </td> <td style="text-align:center;"> 0.005 </td> <td style="text-align:center;"> 8e-04 </td> </tr> <tr> <td style="text-align:center;"> 15-20 </td> <td style="text-align:center;"> 0.006 </td> <td style="text-align:center;"> 9e-04 </td> </tr> </tbody> </table> </td> <td> <table> <thead> <tr> <th style="text-align:center;"> Age </th> <th style="text-align:center;"> coeff </th> <th style="text-align:center;"> std_error </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 15-64 </td> <td style="text-align:center;"> 0.007 </td> <td style="text-align:center;"> 8e-04 </td> </tr> <tr> <td style="text-align:center;"> 15-29 </td> <td style="text-align:center;"> 0.006 </td> <td style="text-align:center;"> 6e-04 </td> </tr> <tr> <td style="text-align:center;"> 15-20 </td> <td style="text-align:center;"> 0.007 </td> <td style="text-align:center;"> 7e-04 </td> </tr> </tbody> </table> </td> </tr> </tbody> </table> - Modest impact on FLFP that is stronger on industrial FLFP and among younger women. -- - At mean export exposure for a sub-district over two decades: - FLFP increases by 3.45 percentage point overall, 6.04 for industrial FLFP. - More than half of industrial FLFP change is from RMG industry. --- # Reproductive behavior <table class="kable_wrapper"> <caption>Influence on marriage rates (left) and fertility (right)</caption> <tbody> <tr> <td> <table> <thead> <tr> <th style="text-align:center;"> Age </th> <th style="text-align:center;"> coeff </th> <th style="text-align:center;"> std_error </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 15-20 </td> <td style="text-align:center;"> -4e-04 </td> <td style="text-align:center;"> 5e-04 </td> </tr> <tr> <td style="text-align:center;"> 21-30 </td> <td style="text-align:center;"> 1e-04 </td> <td style="text-align:center;"> 4e-04 </td> </tr> </tbody> </table> </td> <td> <table> <thead> <tr> <th style="text-align:center;"> Age </th> <th style="text-align:center;"> coeff </th> <th style="text-align:center;"> std_error </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 15-20 </td> <td style="text-align:center;"> 0.0001 </td> <td style="text-align:center;"> 0.0004 </td> </tr> <tr> <td style="text-align:center;"> 21-30 </td> <td style="text-align:center;"> 0.0000 </td> <td style="text-align:center;"> 0.0015 </td> </tr> <tr> <td style="text-align:center;"> 15-20 </td> <td style="text-align:center;"> 0.0036 </td> <td style="text-align:center;"> 0.0023 </td> </tr> </tbody> </table> </td> </tr> </tbody> </table> - Signs as I expected, but not statistically significant. --- # Human capital accumulation <table> <caption>Influence on schooling rates of women</caption> <thead> <tr> <th style="text-align:center;"> Age </th> <th style="text-align:center;"> coeff </th> <th style="text-align:center;"> std_error </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 05-09 </td> <td style="text-align:center;"> 0.0009 </td> <td style="text-align:center;"> 0.0008 </td> </tr> <tr> <td style="text-align:center;"> 10-13 </td> <td style="text-align:center;"> -0.0002 </td> <td style="text-align:center;"> 0.0008 </td> </tr> <tr> <td style="text-align:center;"> 14-19 </td> <td style="text-align:center;"> -0.0013 </td> <td style="text-align:center;"> 0.0011 </td> </tr> </tbody> </table> - Signs as I expected, but not statistically significant. --- # Conclusions - Results indicate that the RMG industry provided opportunities for some of the women to join manufacturing labor force. -- - However, most changes in reproductive and human capital likely driven by other factors. -- .hi[Thank you. Suggestions and comments are very appreciated!]